The Central Bank of the Republic of Turkey, CBRT (Turkish: Türkiye Cumhuriyet Merkez Bankası, TCMB) is the central bank of Turkey. Its responsibilities include conducting monetary and exchange rate policy, managing international reserves of Turkey, as well as printing and issuing banknotes, and establishing, maintaining and regulating payment systems in the country. The CBRT is tasked by law to achieve and maintain price and financial stability in Turkey, and has a mandate to use, by its own discretion, whichever policy instrument at its disposal to reach these objectives. Therefore, it has instrument but not goal independence. Since 2006, the CBRT follows a full-fledged inflation targeting regime(EVDS). For this analysis, I take data from EVDS and Google Trends.
I install some packages before i start out analysis.
library(readxl)
library(ggplot2)
library(dplyr)
library(tidyr)
library(lubridate)
library(RColorBrewer)
library(zoo)
library(data.table)
library(plotly)Three time series data is taken from EVDSand two time series data is taken from EVDS as csv files.
Compared to a few years ago, the current value of the Turkish lira against the US dollar and the euro will benefit tourists on their trip to Turkey, but perhaps not as much as they’d wish. Many Turkish travel companies such as hotels, car rental agencies, tour operators, etc., price their products and services in dollars or euros. Those prices may stay the same. Costs priced in Turkish liras, such as restaurant meals, public transit, and airfares, will probably be lower for visitors converting euros and dollars to liras. Visitor should plans to spend around TRY334 per day on your vacation in Turkey, which is the average daily price based on the expenses of other visitors. Past travelers have spent, on average, TRY73 on meals for one day and TRY22 on local transportation. (9 March 2021)
Firstly, I analysis the average amount of money spent by a tourist in Turkey.
average_expenditure_per_capita<-read_excel("ie360/average_expenditure_per_capita.xlsx")
otel_keyword<-read.csv("ie360/otel_keyword.csv",sep = ",")
istanbul_keyword<-read.csv("ie360/istanbul_keyword.csv",sep = ",")Euro currency is rising increasingly in Turkey. Fluctuations in the economy are the biggest factor in the increase of the euro currency.
Secondly, I analyzed the change of the euro currency against the Turkish lira.
buy_euro_currency<-read_excel("ie360/buy_euro_currency.xlsx")
doviz_keyword<-read.csv("ie360/doviz_keyword.csv",sep = ",")
euro_keyword<-read.csv("ie360/euro_keyword.csv",sep = ",")Today, the use of debit and credit cards has increased considerably. Although the use of cash is not preferred anymore, the lightness and ease of use of the cards have brought people closer to using cards.
Thirdly, I analysis the total amount of use of debit and credit cards in Turkey.
total_debit_card_and_credit_card_spending_amount<-read_excel("ie360/total_debit_card_and_credit_card_spending_amount.xlsx")
enflasyon_keyword<-read.csv("ie360/enflasyon_keyword.csv",sep = ",")
taksit_keyword<-read.csv("ie360/taksit_keyword.csv",sep = ",")Firstly, I converted data into a data table. After that, i checked for duplicated(duplicated) and NA(is.na) values. Lastly i renamed(rename) and glimpsed(glimpse).
average_expenditure_per_capita_new <- setDT(average_expenditure_per_capita)
sum(is.na(average_expenditure_per_capita_new))
sum(duplicated(average_expenditure_per_capita_new))
average_expenditure_per_capita_new <-average_expenditure_per_capita_new%>%rename(Date=Tarih,Expenditure=`TP SGEGI K7`)%>%
mutate(Date=as.Date(as.yearmon(Date)))
average_expenditure_per_capita_new %>% glimpse()
otel_keyword_new<-setDT(otel_keyword)
sum(is.na(otel_keyword_new))
sum(duplicated(otel_keyword_new))
otel_keyword_new <-otel_keyword_new%>%rename(Date=Ay,Hotel=otel...TÃ.rkiye.)%>%
mutate(Date=as.Date(as.yearmon(Date)))
otel_keyword_new %>% glimpse()
istanbul_keyword_new<-setDT(istanbul_keyword)
sum(is.na(istanbul_keyword_new))
sum(duplicated(istanbul_keyword_new))
istanbul_keyword_new <-istanbul_keyword_new%>%rename(Date=Ay,Istanbul=istanbul...TÃ.rkiye.)%>%
mutate(Date=as.Date(as.yearmon(Date)))
istanbul_keyword_new %>% glimpse()I did the same things I did in the first stage
buy_euro_currency_new <- setDT(buy_euro_currency)
sum(is.na(buy_euro_currency_new))
sum(duplicated(buy_euro_currency_new))
buy_euro_currency_new <-buy_euro_currency_new%>%rename(Date=Tarih,Currency=`TP DK EUR A YTL`)%>%
mutate(Date=as.Date(as.yearmon(Date)))
buy_euro_currency_new %>% glimpse()
doviz_keyword_new<-setDT(doviz_keyword)
sum(is.na(doviz_keyword_new))
sum(duplicated(doviz_keyword_new))
doviz_keyword_new <-doviz_keyword_new%>%rename(Date=Ay,Doviz=dÃ.viz...T.rkiye.)%>%
mutate(Date=as.Date(as.yearmon(Date)))
doviz_keyword_new %>% glimpse()
euro_keyword_new<-setDT(euro_keyword)
sum(is.na(euro_keyword_new))
sum(duplicated(euro_keyword_new))
euro_keyword_new <-euro_keyword_new%>%rename(Date=Ay,Euro=Euro...Türkiye.)%>%
mutate(Date=as.Date(as.yearmon(Date)))
euro_keyword_new %>% glimpse()I did the same things I did in the first stage
total_debit_card_and_credit_card_spending_amount_new <- setDT(total_debit_card_and_credit_card_spending_amount)
sum(is.na(total_debit_card_and_credit_card_spending_amount_new))
sum(duplicated(total_debit_card_and_credit_card_spending_amount_new))
total_debit_card_and_credit_card_spending_amount_new <-total_debit_card_and_credit_card_spending_amount_new%>%rename(Date=Tarih,CardExp=`TP KKHARTUT KT1`)%>%
mutate(Date=as.Date(as.yearmon(Date)))
total_debit_card_and_credit_card_spending_amount_new %>% glimpse()
enflasyon_keyword_new<-setDT(enflasyon_keyword)
sum(is.na(enflasyon_keyword_new))
sum(duplicated(enflasyon_keyword_new))
enflasyon_keyword_new <-enflasyon_keyword_new%>%rename(Date=Ay,Inflation=enflasyon...TÃ.rkiye.)%>%
mutate(Date=as.Date(as.yearmon(Date)))
enflasyon_keyword_new %>% glimpse()
taksit_keyword_new<-setDT(taksit_keyword)
sum(is.na(taksit_keyword_new))
sum(duplicated(taksit_keyword_new))
taksit_keyword_new <-taksit_keyword_new%>%rename(Date=Ay,HirePurchase=taksit...TÃ.rkiye.)%>%
mutate(Date=as.Date(as.yearmon(Date)))
taksit_keyword_new %>% glimpse()ggplotly(
ggplot(data=average_expenditure_per_capita_new, aes(x=Date, y=Expenditure, group=1)) +
geom_step(color="red")+
geom_point()+theme(plot.title = element_text(hjust = 0.5))+
labs(
title = "Average Expenditure Per Person (USD) in Turkey between Jan 2015 and December 2020",
x = "Date",
y = "Expenditure"
))ggplotly(ggplot(data=otel_keyword_new, aes(x=Date, y=Hotel, group=1)) +
geom_line(color="blue")+theme(plot.title = element_text(hjust = 0.5))+
labs(
title = "Number of searches for 'Otel' in Turkey between January 2015 and December 2020",
x = "Date",
y = "Number of searches for 'Otel'"
))ggplotly(
ggplot(data=istanbul_keyword_new, aes(x=Date, y=Istanbul, group=1)) +
geom_line(color="red")+theme(plot.title = element_text(hjust = 0.5))+
labs(
title = "Number of searches for 'Istanbul' in Turkey between January 2015 and December 2020",
x = "Date",
y = "Number of searches for 'Istanbul' "
))ggplotly(
ggplot(buy_euro_currency_new,aes(x=Date,y=Currency))+geom_step(color="orange")+theme(plot.title = element_text(hjust = 0.5))+
labs(
title = "Euro (Buying Currency) in Turkey between January 2015 and December 2020",
x = "Date",
y = "Currency"
))ggplotly(
ggplot(data=doviz_keyword_new, aes(x=Date, y=Doviz, group=1)) +
geom_line(color="blue")+theme(plot.title = element_text(hjust = 0.5))+
labs(
title = "Number of searches for 'Doviz' in Turkey between January 2015 and December 2020",
x = "Date",
y = "Number of searches for 'Doviz' "
))ggplotly(
ggplot(data=euro_keyword_new, aes(x=Date, y=Euro, group=1)) +
geom_line(color="red")+theme(plot.title = element_text(hjust = 0.5))+
labs(
title = "Number of searches for 'Euro' in Turkey between January 2015 and December 2020",
x = "Date",
y = "Number of searches for 'Euro' "
))ggplotly(
ggplot(total_debit_card_and_credit_card_spending_amount_new,aes(x=Date,y=CardExp))+geom_step(color="orange")+theme(plot.title = element_text(hjust = 0.5))+
labs(
title = "Total Debit and Credit Card Spending Amount between January 2015 and December 2020",
x = "Date",
y = "Card Expenditure"
))ggplotly(
ggplot(data=enflasyon_keyword_new, aes(x=Date, y=Inflation, group=1)) +
geom_line(color="blue")+theme(plot.title = element_text(hjust = 0.5))+
labs(
title = "Number of searches for 'Enflasyon' in Turkey between January 2015 and December 2020",
x = "Date",
y = "Number of searches for 'Enflasyon'"
))In CPI (2003 = 100) in October 2018, 2.67% compared to the previous month, 22.56% compared to December of the previous year, 25.24% compared to the same month of the previous year and 14% on the twelve-month averages Increased by 90. Since this increase is much higher than normal, it is normal for people to google the word ‘Enflasyon’.
ggplotly(
ggplot(data=taksit_keyword_new, aes(x=Date, y=HirePurchase, group=1)) +
geom_line(color="red")+theme(plot.title = element_text(hjust = 0.5))+
labs(
title = "Number of searches for 'Taksit' in Turkey between January 2015 and December 2020",
x = "Date",
y = "Number of searches for 'Taksit'"
))